BAROMÈTRE SANTÉ JEUNE 2019 (BSJ-2019)
BAROMÈTRE SANTÉ JEUNE 2019 (BSJ-2019)¶
Source : https://data.gouv.nc/explore/dataset/bsj-2019/information
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Successfully built metaphone pystache python-Levenshtein
Installing collected packages: regex, python-stdnum, python-crfsuite, ply, metaphone, Werkzeug, typing-extensions, tqdm, toolz, python-Levenshtein, pystache, pillow, numpy, multidict, locket, kiwisolver, jsonpath-ng, joblib, itsdangerous, fsspec, frozenlist, fonttools, cycler, cloudpickle, async-timeout, yarl, varname, scipy, pydantic, partd, pandas, nltk, matplotlib, flask, bokeh, aiosignal, wordcloud, seaborn, flask_cors, dask, aiohttp, dataprep
Successfully installed Werkzeug-2.1.2 aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 bokeh-2.4.2 cloudpickle-2.0.0 cycler-0.11.0 dask-2021.12.0 dataprep-0.4.3 flask-2.1.2 flask_cors-3.0.10 fonttools-4.33.3 frozenlist-1.3.0 fsspec-2022.3.0 itsdangerous-2.1.2 joblib-1.1.0 jsonpath-ng-1.5.3 kiwisolver-1.4.2 locket-1.0.0 matplotlib-3.5.2 metaphone-0.6 multidict-6.0.2 nltk-3.7 numpy-1.22.3 pandas-1.4.2 partd-1.2.0 pillow-9.1.0 ply-3.11 pydantic-1.9.0 pystache-0.6.0 python-Levenshtein-0.12.2 python-crfsuite-0.9.8 python-stdnum-1.17 regex-2021.11.10 scipy-1.7.1 seaborn-0.11.2 toolz-0.11.2 tqdm-4.64.0 typing-extensions-4.2.0 varname-0.8.3 wordcloud-1.8.1 yarl-1.7.2
WARNING: You are using pip version 22.0.4; however, version 22.1 is available.
You should consider upgrading via the '/opt/hostedtoolcache/Python/3.8.12/x64/bin/python -m pip install --upgrade pip' command.
!wget "https://data.gouv.nc/explore/dataset/bsj-2019/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/barometre_jeune_2019.csv
--2022-05-13 04:02:29-- https://data.gouv.nc/explore/dataset/bsj-2019/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)...
13.211.119.48, 13.55.171.246
Connecting to data.gouv.nc (data.gouv.nc)|13.211.119.48|:443...
connected.
HTTP request sent, awaiting response...
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/barometre_jeune_2019.csv’
data/baro [<=> ] 0 --.-KB/s
data/barom [ <=> ] 100.68K 258KB/s
data/barome [ <=> ] 420.61K 539KB/s
data/baromet [ <=> ] 1.33M 1.36MB/s
data/barometr [ <=> ] 3.43M 2.87MB/s
data/barometre [ <=> ] 4.68M 2.97MB/s
data/barometre_ [ <=> ] 5.56M 2.96MB/s
data/barometre_jeun [ <=> ] 6.44M 3.25MB/s in 2.0s
2022-05-13 04:02:33 (3.25 MB/s) - ‘data/barometre_jeune_2019.csv’ saved [6748346]
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from dataprep.eda import plot
df = pd.read_csv("data/barometre_jeune_2019.csv")
df.head()
| N°Obs | Date_Enreg | Province_Etab | Secteur_Etab | Niveau_Scolaire | CS1 | CS2 | CS3 | Age | CS5 | ... | PR1h | PR2a | PR2b | PR2c | PR2d | PR2e | PR2f | PR2g | PR2h | Pond | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2019-01-07 | Province Sud | Public | 5EME | Une fille | Collège | 5ème | 12 | Wallisienne, Futunienne | ... | Plutot bien informe | Je ne connais pas le risque | Risque plutot eleve | Risque nul | Je ne connais pas le risque | Je ne connais pas le risque | Je ne connais pas le risque | Risque tres eleve | Je ne connais pas le risque | 14.818182 |
| 1 | 2 | 2019-01-07 | Province Sud | Public | 6EME | Un garçon | Collège | 6ème | 12 | Kanak;Wallisienne, Futunienne | ... | Je ne sais pas | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | 14.761290 |
| 2 | 3 | 2019-01-07 | Province Sud | Public | 5EME | Une fille | Collège | 5ème | 13 | Kanak;Wallisienne, Futunienne | ... | Je ne sais pas | Risque plutot faible | Risque plutot faible | Risque plutot faible | Risque plutot faible | Risque plutot faible | Risque plutot faible | Risque plutot faible | Risque plutot faible | 11.261438 |
| 3 | 4 | 2019-01-07 | Province Sud | Public | 5EME | Une fille | Collège | 5ème | 12 | Kanak;Ni-Vanuatu;Autres | ... | Plutot bien informe | Risque plutot eleve | Risque tres eleve | Risque plutot eleve | Risque tres eleve | Je ne connais pas le risque | Risque tres eleve | Risque tres eleve | Risque tres eleve | 14.818182 |
| 4 | 5 | 2019-01-07 | Province Sud | Public | 6EME | Un garçon | Collège | 6ème | 12 | Wallisienne, Futunienne | ... | Plutot bien informe | Je ne sais pas ce que c'est | Risque plutot faible | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | Je ne sais pas ce que c'est | 14.761290 |
5 rows × 163 columns
plot(df)
Dataset Statistics
| Number of Variables | 163 |
|---|---|
| Number of Rows | 3435 |
| Missing Cells | 189125 |
| Missing Cells (%) | 33.8% |
| Duplicate Rows | 0 |
| Duplicate Rows (%) | 0.0% |
| Total Size in Memory | 32.4 MB |
| Average Row Size in Memory | 9.7 KB |
| Variable Types |
|
Dataset Insights
| N°Obs is uniformly distributed | Uniform |
|---|---|
| CS5_Autre has 3134 (91.24%) missing values | Missing |
| VS5_Autre has 3221 (93.77%) missing values | Missing |
| ES3 has 836 (24.34%) missing values | Missing |
| ES3b has 2416 (70.33%) missing values | Missing |
| ES4 has 962 (28.01%) missing values | Missing |
| ES4b has 2439 (71.0%) missing values | Missing |
| ES7 has 2663 (77.53%) missing values | Missing |
| ES7_Autre has 3357 (97.73%) missing values | Missing |
| ES8 has 1656 (48.21%) missing values | Missing |
Dataset Insights
| ES9 has 2029 (59.07%) missing values | Missing |
|---|---|
| ES9b has 2390 (69.58%) missing values | Missing |
| AS3 has 1805 (52.55%) missing values | Missing |
| AS3_Autre has 3227 (93.94%) missing values | Missing |
| AL4 has 1528 (44.48%) missing values | Missing |
| AL4_Autre has 3321 (96.68%) missing values | Missing |
| AP3 has 487 (14.18%) missing values | Missing |
| AP4 has 487 (14.18%) missing values | Missing |
| AP4_Autre has 3261 (94.93%) missing values | Missing |
| AP5 has 2950 (85.88%) missing values | Missing |
Dataset Insights
| AP5_Autre has 3399 (98.95%) missing values | Missing |
|---|---|
| SM4_Autre has 3152 (91.76%) missing values | Missing |
| SM5 has 903 (26.29%) missing values | Missing |
| CV4a has 1843 (53.65%) missing values | Missing |
| CV4b has 1842 (53.62%) missing values | Missing |
| VI2 has 2882 (83.9%) missing values | Missing |
| VI2_Autre has 3364 (97.93%) missing values | Missing |
| VI3 has 2883 (83.93%) missing values | Missing |
| VI3_Autre has 3406 (99.16%) missing values | Missing |
| VI4 has 2885 (83.99%) missing values | Missing |
Dataset Insights
| VI4_Autre has 3384 (98.52%) missing values | Missing |
|---|---|
| VI6 has 2271 (66.11%) missing values | Missing |
| VI6_Autre has 3320 (96.65%) missing values | Missing |
| VI7 has 2272 (66.14%) missing values | Missing |
| VI7_Autre has 3396 (98.86%) missing values | Missing |
| VI8 has 2273 (66.17%) missing values | Missing |
| VI8_Autre has 3364 (97.93%) missing values | Missing |
| TB2 has 1937 (56.39%) missing values | Missing |
| TB3 has 1914 (55.72%) missing values | Missing |
| TB4 has 2494 (72.61%) missing values | Missing |
Dataset Insights
| TB5 has 2494 (72.61%) missing values | Missing |
|---|---|
| TB6 has 2494 (72.61%) missing values | Missing |
| TB7 has 2494 (72.61%) missing values | Missing |
| AO2a has 2435 (70.89%) missing values | Missing |
| AO2b has 2211 (64.37%) missing values | Missing |
| AO3a has 2200 (64.05%) missing values | Missing |
| AO3b has 2202 (64.1%) missing values | Missing |
| AO3c has 2200 (64.05%) missing values | Missing |
| AO3d has 2202 (64.1%) missing values | Missing |
| AO4 has 2200 (64.05%) missing values | Missing |
Dataset Insights
| AO5 has 2620 (76.27%) missing values | Missing |
|---|---|
| AO6 has 2621 (76.3%) missing values | Missing |
| AO7 has 2620 (76.27%) missing values | Missing |
| AO8 has 2620 (76.27%) missing values | Missing |
| AO9 has 2201 (64.08%) missing values | Missing |
| AO10 has 2701 (78.63%) missing values | Missing |
| AO11 has 2692 (78.37%) missing values | Missing |
| CN2 has 2443 (71.12%) missing values | Missing |
| CN3 has 2431 (70.77%) missing values | Missing |
| CN4 has 2929 (85.27%) missing values | Missing |
Dataset Insights
| CN5 has 2929 (85.27%) missing values | Missing |
|---|---|
| CN6 has 2930 (85.3%) missing values | Missing |
| CN7 has 2930 (85.3%) missing values | Missing |
| AD3 has 3343 (97.32%) missing values | Missing |
| SS1 has 40 (1.16%) missing values | Missing |
| SS2 has 41 (1.19%) missing values | Missing |
| SS3 has 2527 (73.57%) missing values | Missing |
| SS4 has 2513 (73.16%) missing values | Missing |
| SS5 has 2850 (82.97%) missing values | Missing |
| SS6 has 2850 (82.97%) missing values | Missing |
Dataset Insights
| SS6_Autre has 3415 (99.42%) missing values | Missing |
|---|---|
| SS7 has 3149 (91.67%) missing values | Missing |
| SS7_Autre has 3372 (98.17%) missing values | Missing |
| SS8 has 2513 (73.16%) missing values | Missing |
| SS9 has 3138 (91.35%) missing values | Missing |
| SS10 has 2513 (73.16%) missing values | Missing |
| SS11 has 48 (1.4%) missing values | Missing |
| SS12 has 2196 (63.93%) missing values | Missing |
| PR1a has 65 (1.89%) missing values | Missing |
| PR1b has 65 (1.89%) missing values | Missing |
Dataset Insights
| PR1c has 65 (1.89%) missing values | Missing |
|---|---|
| PR1d has 65 (1.89%) missing values | Missing |
| PR1e has 65 (1.89%) missing values | Missing |
| PR1f has 65 (1.89%) missing values | Missing |
| PR1g has 65 (1.89%) missing values | Missing |
| PR1h has 65 (1.89%) missing values | Missing |
| PR2a has 76 (2.21%) missing values | Missing |
| PR2b has 76 (2.21%) missing values | Missing |
| PR2c has 76 (2.21%) missing values | Missing |
| PR2d has 76 (2.21%) missing values | Missing |
Dataset Insights
| PR2e has 76 (2.21%) missing values | Missing |
|---|---|
| PR2f has 76 (2.21%) missing values | Missing |
| PR2g has 76 (2.21%) missing values | Missing |
| PR2h has 76 (2.21%) missing values | Missing |
| ES9 is skewed | Skewed |
| SS3 is skewed | Skewed |
| CS5 has a high cardinality: 103 distinct values | High Cardinality |
| CS5_Autre has a high cardinality: 149 distinct values | High Cardinality |
| VS5 has a high cardinality: 51 distinct values | High Cardinality |
| VS5_Autre has a high cardinality: 178 distinct values | High Cardinality |
Dataset Insights
| ES7 has a high cardinality: 55 distinct values | High Cardinality |
|---|---|
| ES7_Autre has a high cardinality: 69 distinct values | High Cardinality |
| AS3 has a high cardinality: 181 distinct values | High Cardinality |
| AS3_Autre has a high cardinality: 185 distinct values | High Cardinality |
| AL4 has a high cardinality: 71 distinct values | High Cardinality |
| AL4_Autre has a high cardinality: 113 distinct values | High Cardinality |
| AP4 has a high cardinality: 456 distinct values | High Cardinality |
| AP4_Autre has a high cardinality: 170 distinct values | High Cardinality |
| AP5 has a high cardinality: 128 distinct values | High Cardinality |
| SM4 has a high cardinality: 97 distinct values | High Cardinality |
Dataset Insights
| SM4_Autre has a high cardinality: 201 distinct values | High Cardinality |
|---|---|
| VI2_Autre has a high cardinality: 63 distinct values | High Cardinality |
| VI3 has a high cardinality: 51 distinct values | High Cardinality |
| VI4 has a high cardinality: 132 distinct values | High Cardinality |
| VI6_Autre has a high cardinality: 91 distinct values | High Cardinality |
| VI7 has a high cardinality: 80 distinct values | High Cardinality |
| VI8 has a high cardinality: 224 distinct values | High Cardinality |
| VI8_Autre has a high cardinality: 66 distinct values | High Cardinality |
| TB6 has a high cardinality: 55 distinct values | High Cardinality |
| AO8 has a high cardinality: 69 distinct values | High Cardinality |
Dataset Insights
| AD3 has a high cardinality: 67 distinct values | High Cardinality |
|---|---|
| SS7_Autre has a high cardinality: 55 distinct values | High Cardinality |
| SS12 has a high cardinality: 76 distinct values | High Cardinality |
| ES3b has constant value "Je ne sais pas" | Constant |
| ES4b has constant value "Je ne sais pas" | Constant |
| Date_Enreg has constant length 10 | Constant Length |
| CS1 has constant length 9 | Constant Length |
| Age has constant length 2 | Constant Length |
| ES3b has constant length 14 | Constant Length |
| ES4b has constant length 14 | Constant Length |
Dataset Insights
| ES5 has constant length 3 | Constant Length |
|---|---|
| ES6 has constant length 3 | Constant Length |
| ES8 has constant length 3 | Constant Length |
| AS2 has constant length 3 | Constant Length |
| BD2 has constant length 3 | Constant Length |
| BD3 has constant length 3 | Constant Length |
| BD4 has constant length 3 | Constant Length |
| SM6 has constant length 3 | Constant Length |
| SM7 has constant length 3 | Constant Length |
| VI1 has constant length 3 | Constant Length |
Dataset Insights
| VI5 has constant length 3 | Constant Length |
|---|---|
| TB1 has constant length 3 | Constant Length |
| TB3 has constant length 3 | Constant Length |
| TB7 has constant length 3 | Constant Length |
| AO4 has constant length 3 | Constant Length |
| AO9 has constant length 3 | Constant Length |
| CN1 has constant length 3 | Constant Length |
| CN3 has constant length 3 | Constant Length |
| CN7 has constant length 3 | Constant Length |
| AD2 has constant length 3 | Constant Length |
Dataset Insights
| VI3_Autre has all distinct values | Unique |
|---|---|
| VI7_Autre has all distinct values | Unique |
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